However, when solving the benchmark problems available here there is an enormous performance difference between the two - Walksat is much faster than minisat for these problems. Why is this?
Yes, there is a major difference between MiniSAT and WalkSAT. First, let's clarify - MiniSAT is a specific implementation of the generic class of DPLL/CDCL algorithms which use backtracking and clause learning, whereas WalkSAT is the general name for an algorithm which alternates between greedy steps and random steps.
In general DPLL/CDCL is much faster on structured SAT instances while WalkSAT is faster on random k-SAT. Industrial and applied SAT instances tend to have a lot of structure, so DPLL/CDCL is dominant in most modern SAT solvers. Instance to instance one technique may win out, though, which is one reason why portfolio solvers have become popular.
I take a lot of issue with your claim that WalkSAT is much faster than MiniSAT on the instances on that page. For one thing, there are gigabytes of SAT instances there - how many did you try comparing them on? WalkSAT is not at all competitive on most structured instances which is why it's not often seen in competitions.
On a side note - Vijay is right that MiniSAT is still relevant. Actually, because it's open source and well-written, MiniSAT is the solver to beat in order to show that a given optimization has promise. Many people tweak MiniSAT itself to showcase their optimizations - take a look at the "MiniSAT hack" category in the recent SAT competitions.
There is an enormous difference between sat instances. SAT solver $A$ might perform well on the class $X$ of instances, but poorly on the class $Y$ of instances, while solver $B$ performs well on class $Y$ and poorly on class $X$.
A good paper to read on this topic is this one by Nudelman et al. The whole point of the paper is to determine easy-to-compute features of SAT instances that can tell you which algorithms are likely to perform well and which aren't. Using this technique it's possible to build a portfolio-based algorithm that will quickly analyze a problem instance, then solve the instance with the most appropriate algorithm. There's a progression of papers that follows that one; googling SATzilla will turn up lots of reading material.
If you're wondering why SAT solver $A$ might be better than solver $B$ on all instances, well, I guess that's progress :). If you want to know what specifically makes a solver good, the answer could probably be turned into several doctoral theses. I suggest you start with that paper of Nudelman et al.