Fine-scale characterization of the geometry of the folding patterns of the brain is a key processing step in neuroscience, with high impact applications such as for uncovering biomarkers indicative of a neurological pathology. Sulcal graphs constitute relevant representations of the complex and variable geometry of the cortex of individual brains. Comparing sulcal graphs is challenging due to variations across subjects in the number of nodes, graph topology and attributes (on both nodes and edges). Graph matching experiments on real data are limited by the absence of ground truth. In this paper we propose to generate synthetic graphs to benchmark graph matching methods and assess their robustness to noise on attributes and to the presence of un-matchable nodes. Three multi-graph matching methods are compared to one pairwise approach in various simulation settings, showing that good matching performances can be achieved even with highly perturbed sulcal graphs. An experiment on real data from a population of 134 subjects further unveil large performance differences across matching methods.