An unresolved question in network neuroscience is the quantification of reconfiguration in functional networks in response to varying cognitive demands. We propose that a mesoscopic generalizable framework would be most apt to investigate the breadth of functional (re-)configurations. We propose a 2D network morphospace using novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE), that characterize the topology of mesoscopic structures and the flow of information within and between them. This framework captures the behavior of a reference set of functional networks (FNs) with changing mental states. We show that this morphospace is sensitive to different FNs, cognitive tasks and subjects. We propose that functional connectivity changes in FNs may be categorized into three different types of reconfigurations: i) Network Configural Breadth, ii) Task-to-Task transitional reconfiguration, and iii) Within-Task reconfiguration; and quantify the Network Configural Breadth across different tasks. In essence, we put forth a framework that can be used to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity; a tool that can also quantify the changes that result from such an exploration, as the brain switches between mental states.