Neutrality of genetic programming Boolean function landscapes is investigated in this paper. Compared with some well known contributions on the same issue, we define new measures that help characterizing neutral landscapes, we use a new sampling methodology, which captures features that are disregarded by uniform random sampling, we introduce new genetic operators to define the neighborhood of tree structures and we compare the fitness landscape induced by different sets of functional operators. This study indicates the existence of a relationship between our neutrality measures and the performance of genetic programming for the problems studied.