Still trying to understand the implementation of the linear vs RBF SVM.

I get the RBF is used when the data is not linearly separable.

My question is:

Given a data set with a multiple class- is there a way to know, a priori, if to use the linear or RBF? (I guess that is another way of asking how do I know, a priori, if the data is linear separable?)

If in practice, if I should run both and see which performs best- what am I looking for to determine which kernel performs best? Just some measure of accuracy?

Thanks