Data-flow oriented embedded systems, such as automotive systems used to render HMI (e.g., instrument clusters, infotainments), are increasingly built from highly variable specifications while targeting different constrained hardware platforms configurable in a fine-grained way. These variabilities at two different levels lead to a huge number of possible embedded system solutions, which functional feasibility is extremely complex and tedious to predetermine. In this paper, we propose a tooled approach that capture high level specifications as variable dataflows, and targeted platforms as variable component models. Dataflows can then be mapped onto platforms to express a specification of such variability-intensive systems. The proposed solution transforms this specification into structural and behavioral variability models and reuses automated reasoning techniques to explore and assess the functional feasibility of all variants in a single run. We also report on the validation of the proposed approach. A qualitative evaluation has been conducted on an industrial case study of automotive instrument cluster, while a quantitative one is reported on large generated datasets.