The integration of static parameters into Synchronous Dataflow (SDF) models enables the customization of an application functional and non-functional behaviours. However, these parameter values are generally set by the developer for a manual Design Space Exploration (DSE). Instead of a single value, moldable parameters accept a set of alternative values, representing all possible configurations of the application. The DSE is responsible for selecting the best parameter values to optimize a set of criteria such as latency, energy, or memory footprint. However, the DSE process explodes in complexity with the number of parameters and their possible values. In this paper, we study an automated DSE algorithm exploring multiple configurations of a dataflow application. Our experiments show that: 1) Only limited sets of configurations lead to Pareto-optimal solutions in a multi-criteria optimization scenario. 2) How individual parameters impact on optimization criteria are determined accurately from a limited subset of design points. The approach was evaluated on three image processing applications having from hundreds to thousands configurations.