That a Bayesian NN "defines a distribution over neural networks" means that a Bayesian NN is a NN with priors on the weights. So it's not that there is some special similarity to GPs. BNNs are like any other Bayesian model, but now a NN is the likelihood.

The situation is usually flipped with Bayesian GP models -- a GP is usually used as a prior on the linear predictor.